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Research over the past few decades has assumed the richness (number ofspecies) to be the primary driver of the biodiversity and ecosystem function(BEF) relationship. However, biodiversity is multi-dimensional, and richnessalone does not capture all its attributes. Diversity-Interactions modelling isa regression-based framework that models the biodiversity and ecosystemfunction relationship by incorporating species-specific effects along with theinteractions between species. The species interactions in aDiversity-Interactions model can take several different forms ranging incomplexity from a single interaction term (assuming all pairs of speciesinteract in the same way) to many interaction terms (e.g. assuming a separateinteraction for all pairs of species). The specification of the interactionsmay also include a non-linear parameter ({\theta}) as an exponent to theproduct of the species proportions to allow for non-linear relationship betweenthe response and species interactions, giving rise to GeneralizedDiversity-Interactions modelling. The structure of the interaction termsdescribes the underlying biological processes and thus the selection of acorrect structure is important. The inclusion of {\theta} introduces complexityto the model selection process: we could (a) select the interaction structurefirst by assuming {\theta}=1 and then estimate {\theta}, or (b) estimate{\theta} first and then select the appropriate interaction structure by fixing{\theta}, or (c) test for {\theta} and its inclusion for each interactionstructure. It is also unknown whether {\theta} is robust to changes in theunderlying interaction structure. Using a simulation study, we test therobustness of {\theta} and compare multiple model selection approaches toidentify an optimal and computationally efficient model selection procedure forGeneralized Diversity-Interactions models.

Estimation of the non-linear parameter in Generalised Diversity-Interactions models is unaffected by change in structure of the interaction terms